{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 5a: Basic Solute Transport: Solute Introduced through a Constant Flux Boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook represents the steps to replicate example 5a from: * David Rassam, Jirka Šimůnek, Dirk Mallants,and Martinus Th. van Genuchten, The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media* \\\n",
    "Tutorial \\\n",
    "Version 1.00, July 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example provides insctructions to create a Pydrus model that simulates water flow and solute transport through a 300-cm deep uniform soil profile. The soil profile is initially solute free. Water is infiltrated into the soil at a  constant flux of 5 cm/d. The infiltrating water a uni solute concentration during the first 10 days and is solute free during the second 10 days. \\\n",
    "The upper boundary and lower boundary are represented with: \n",
    "\n",
    "* Upper BC: Constant Flux (rtop = 5).\n",
    "* Bottom BC: Free Drainage (groundwater table is assumed to be deep enough to not influence water redistributions in the soil profile). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the Pydrus package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pydrus as ps\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create the basic model & add time information basic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory example_5a created\n"
     ]
    }
   ],
   "source": [
    "# Folder for Hydrus files to be stored\n",
    "ws = \"example_5a\"\n",
    "# Path to folder containing hydrus.exe \n",
    "exe = os.path.join(os.getcwd(),\"../../source/hydrus.exe\")  \n",
    "# Description\n",
    "desc = \"Solute Introduced through a Constant Flux Boundary\"\n",
    "# Create model\n",
    "ml = ps.Model(exe_name=exe, ws_name=ws, name=\"model\", description=desc, \n",
    "              mass_units=\"mmol\", time_unit=\"days\", length_unit=\"cm\")\n",
    "#ml.basic_info[\"lFlux\"] = True\n",
    "ml.basic_info[\"lShort\"] = False\n",
    "time=np.arange(2,22,2)\n",
    "times = ml.add_time_info(print_times=True,tmax = 20, print_array=time, \n",
    "                         dt=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Add processes and materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">water</th>\n",
       "      <th colspan=\"4\" halign=\"left\">solute</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>thr</th>\n",
       "      <th>ths</th>\n",
       "      <th>Alfa</th>\n",
       "      <th>n</th>\n",
       "      <th>Ks</th>\n",
       "      <th>l</th>\n",
       "      <th>bulk.d</th>\n",
       "      <th>DisperL</th>\n",
       "      <th>frac</th>\n",
       "      <th>mobile_wc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.078</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1.56</td>\n",
       "      <td>24.96</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   water                                solute                       \n",
       "     thr   ths   Alfa     n     Ks    l bulk.d DisperL frac mobile_wc\n",
       "1  0.078  0.43  0.036  1.56  24.96  0.5    1.5      30    1         0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.add_waterflow(top_bc=1, bot_bc=4, linitw=False, rtop=-5)\n",
    "ml.add_solute_transport(tpulse=10, maxit=1)\n",
    "\n",
    "m = ml.get_empty_material_df(n=1)\n",
    "m.loc[[1, 1]] = [0.078, 0.43, 0.036, 1.56, 24.96, 0.5, 1.5, 30, 1, 0]\n",
    "ml.add_material(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Add profile information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "elements = 100  # Disctretize soil column into n elements\n",
    "depth = -300  # Depth of the soil column\n",
    "ihead = -100  # Determine initial Pressure Head\n",
    "# Create Profile\n",
    "profile = ps.create_profile(bot=depth, dx=abs(depth / elements), \n",
    "                            h=ihead,sconc=0)\n",
    "ml.add_profile(profile) # Add the profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Add observation nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml.add_obs_nodes([0, -150, -300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Add atmosphere boundary conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc = {\"tAtm\": (10, 20), \"Prec\": (5, 5), \"cTop\": (1, 0)}\n",
    "atm = pd.DataFrame(bc, index=(3, 30))\n",
    "ml.add_atmospheric_bc(atm, hcrits=0, hcrita=100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Add solutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol1 = ml.get_empty_solute_df()\n",
    "sol1[\"beta\"] = 1\n",
    "ml.add_solute(sol1, difw=0, difg=0, top_conc=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Write hydrus input files and run hydrus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml.write_input()\n",
    "rs = ml.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213d360e128>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.obs_points(data=\"Concentration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213d56661d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(\"Concentration\", times=[10, 14, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213d5761668>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df = ml.read_solutes()\n",
    "df.columns\n",
    "fig,ax=plt.subplots()\n",
    "df = df.set_index(pd.to_numeric(df.index, errors='coerce'))\n",
    "df.plot(y=\"Sum(cvTop)\", ax=ax, use_index=True)\n",
    "df.plot(y=\"Sum(cvBot)\", ax=ax, use_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
